Product Taxonomy
The 6,544-node product/segment classification tree (dict_product_rs) that SAM segment facts and the supply-chain graph are expressed in.
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Product Taxonomy
taxonomySource DataThe 6,544-node product/segment classification tree (dict_product_rs) that SAM segment facts and the supply-chain graph are expressed in.
Daily source stitch · data frontier 2026-07-31
9. Field reference — every column
dict_product_rs
Table · pick one of 1
dict_product_rs
dict_product_rs — 12 columns · grain (primary key): code
| Column | Name | Type | Null | Description |
|---|---|---|---|---|
code ·PK | Product code | string | no | A node in ChinaScope's 6,544-code product taxonomy — the standard that SAM segment revenue/cost/profit and the supply-chain graph are expressed in. This is the vocabulary that makes product-level data comparable across companies. |
id | Product node ID | string | no | ChinaScope's internal identifier for the taxonomy node. |
name | Product name (CN) | string | yes | The product/segment name in Chinese. |
name_en | Product name (EN) | string | yes | The product/segment name in English (full coverage). |
parent | Parent node | string | yes | The parent product node — the edge that gives the taxonomy its tree shape. Null at the top level. |
ancestors | Ancestor path | string | yes | The full path from the taxonomy root to this product, so any product can be aggregated to any level of the tree in one step. |
valid | Valid flag | string | yes | Marks whether the node is currently in use (all nodes are currently valid). |
industry_code | Linked industry code | string | yes | The industry code this product maps to — the bridge that ties the product tree to the industry classification (dict_industry). Populated on 6,542 of the 6,544 nodes (all but two). |
rem_szh | Remark (CN) | string | yes | A short definitional note describing what the product category includes, in Chinese — resolves where a borderline product belongs. |
rem_en | Remark (EN) | string | yes | The definitional note in English. |
product_level | Product level | string | yes | The depth of this product in the taxonomy (1 = broadest category, up to 9 = most specific). Filter to one level for a consistent granularity. |
source_publish_date | Public-record date | date | no | The date this record's current state became public. |
Sample row:
code: PP001
id: 1838
name: 个人用品
name_en: Personal Products
parent: (null — top level)
ancestors: (null — top level)
valid: 1
industry_code: CSF_30302010
rem_szh: 包括个人护理用品以及美容护理用品。
rem_en: Includes personal care products and beauty-care products.
product_level: 1
source_publish_date: 2023-11-02
Sourced as-delivered from ChinaScope, with resolved codes and structural links computed by Numinor. Licensing passes through to you; fields are served exactly as the vendor issues them.
Access & FAQ
- How do I buy just a few fields?
- Check the fields you want and you'll see your running subtotal. Select every field and the whole-set price applies automatically. Add them to your basket and check out — this dataset, or fields combined across several.
- Can I evaluate these fields in the Matrix before I buy?
- Yes — load the actual source tables in the Matrix and analyze them with an AI agent (point-in-time lagged) to check the fields fit your use case before you buy. Once your selection is activated, export the fields you choose via the API.
- How is it delivered?
- Apache Parquet over a signed-URL REST API, the same pipe as Construct Data. The free Matrix sandbox serves a time-delayed view for evaluation.
- When is each update available, and how reliable is it?
- Source data refreshes on the ChinaScope cadence and is delivered through the same signed-URL pipeline as Construct Data, with the same manifest/status behaviour. Evaluate freshness yourself in the Matrix before you commit.
- Why are some fields free and others cost thousands?
- Price tracks non-replicability. Resolved codes and structural links are the moat; machine scores and raw text are cheap; identifier keys are free. Figures are priced per table; codes are charged once per dataset.
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